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Journal articles on the topic 'Systèmes de tutoring intelligent'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Amastini, Fitria. "Intelligent Tutoring System." Jurnal ULTIMA InfoSys 5, no. 1 (2014): 1–7. http://dx.doi.org/10.31937/si.v5i1.212.

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Intelligent Tutoring System is a tutor behaviour system which can be used as an alternative goal for interactive e-learning and distant learning. This system can provide an adaptive system to support student’s learning and retention process based on their characteristic and needed. There are development method such as bayesian network, and neural network that can build fundamental component of Intelligent Tutoring System. This paper will give some concepts and examples for implementing those method from other papers. Index Terms—intelligent tutoring system, artificial intelligent, neural

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2

Bradáč, Vladimír, and Kateřina Kostolányová. "Intelligent Tutoring Systems." Journal of Intelligent Systems 26, no. 4 (2017): 717–27. http://dx.doi.org/10.1515/jisys-2015-0144.

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AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both a

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3

Anderson, J. R., C. F. Boyle, and B. J. Reiser. "Intelligent Tutoring Systems." Science 228, no. 4698 (1985): 456–62. http://dx.doi.org/10.1126/science.228.4698.456.

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4

ROSS, P. "Intelligent tutoring systems." Journal of Computer Assisted Learning 3, no. 4 (1987): 194–203. http://dx.doi.org/10.1111/j.1365-2729.1987.tb00331.x.

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5

Maher, Mary Lou. "Intelligent tutoring systems." Artificial Intelligence in Engineering 2, no. 1 (1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90075-6.

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6

Auguste, Donna. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (1985): 233–38. http://dx.doi.org/10.1016/0004-3702(85)90033-5.

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7

Stefik, Mark. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (1985): 238–45. http://dx.doi.org/10.1016/0004-3702(85)90034-7.

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8

S.B., Bele1 &. Miss.S.K. Totade2. "INTELLIGENT TUTORING SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 141–44. https://doi.org/10.5281/zenodo.1229026.

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The term "intelligent” refers to a system with the ability to know what to teach, when to teach and how to teach. Intelligent Tutoring System (ITS) is a branch of Artificial Intelligence (AI) that attempts to simulate a “humanlike" tutoring capabilities.  Such tutoring system can be effective because it can answer to the specific needs of student,which will be guide for to slow learners, to  challenge rapid learners and to monitor the progress of each student as well as establishing a training plan very well. IT has the ability to understand, learn, and solve pr

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Petrovica, Sintija. "Tutoring Process in Emotionally Intelligent Tutoring Systems." International Journal of Technology and Educational Marketing 4, no. 1 (2014): 72–85. http://dx.doi.org/10.4018/ijtem.2014010106.

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Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring p

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10

Servan-Schreiber, David, and Yitzchak M. Binik. "Extending the intelligent tutoring system paradigm: Sex therapy as intelligent tutoring." Computers in Human Behavior 5, no. 4 (1989): 241–59. http://dx.doi.org/10.1016/0747-5632(89)90003-4.

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11

Angelides, Marios C., and Amelia K. Y. Tong. "Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning." Journal of Information Technology 10, no. 1 (1995): 52–62. http://dx.doi.org/10.1177/026839629501000107.

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Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutor

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12

Suppes, Patrick. "Intelligent Tutoring, but Not Intelligent Enough." Contemporary Psychology: A Journal of Reviews 35, no. 7 (1990): 648–50. http://dx.doi.org/10.1037/028791.

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13

Tafazoli, Dara, Elena Gómez María, and Cristina A. Huertas Abril. "Intelligent Language Tutoring System." International Journal of Information and Communication Technology Education 15, no. 3 (2019): 60–74. http://dx.doi.org/10.4018/ijicte.2019070105.

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Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language le

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14

Del Soldato, Teresa. "Intelligent Tutoring Systems 92." AI Communications 5, no. 4 (1992): 215–16. http://dx.doi.org/10.3233/aic-1992-5411.

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Ksemidov, B. S., and K. K. Abgaryan. "Adaptive Intelligent Tutoring System." Моделирование и анализ данных 14, no. 2 (2024): 152–65. http://dx.doi.org/10.17759/mda.2024140211.

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<p>The goal of the work is to create a modern adaptive intelligent system using current machine learning technologies to automate a significant part of the teacher&rsquo;s work. Existing intelligent systems, the purpose of which is to train students to work in various subject areas, currently have a set of various disadvantages, for example, the need to prepare educational material in a given format, which is sometimes a very labor-intensive task. In addition, in such systems there is a need to assess knowledge to correct the training plan for students, which requires various practic

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16

Woolf, Beverly Park. "Intelligent multimedia tutoring systems." Communications of the ACM 39, no. 4 (1996): 30–31. http://dx.doi.org/10.1145/227210.227217.

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17

Nkambou, Roger, and Froduald Kabanza. "Designing intelligent tutoring systems." ACM SIGCUE Outlook 27, no. 2 (2001): 46–60. http://dx.doi.org/10.1145/381234.381246.

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18

Yazdani, M. "Intelligent tutoring systems survey." Artificial Intelligence Review 1, no. 1 (1986): 43–52. http://dx.doi.org/10.1007/bf01988527.

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19

Son, Taekwon. "Artificial Intelligence in Mathematics Education: A Systematic Literature Review on Intelligent Tutoring Systems." Korean Society of Educational Studies in Mathematics - Journal of Educational Research in Mathematics 34, no. 2 (2024): 187–208. http://dx.doi.org/10.29275/jerm.2024.34.2.187.

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This study synthesized 63 empirical studies integrating intelligent tutoring systems in mathematics education from 2003 to 2023 and systematically examined trends, effects, and limitations. As a result, research interest in intelligent teaching systems has continued to increase, but most of the research has been conducted in the fields of education and engineering. The intelligent tutoring systems were mainly applied at the elementary school level, focusing on numbers and arithmetic, algebra, and geometry. Most studies reported the positive effects of intelligent teaching systems, and six limi

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Ilić, Jelena, Mirjana Ivanović, and Aleksandra Klašnja-Milićević. "The impact of intelligent tutoring systems and artificial intelligence on students' motivation and achievement in STEM education: A systematic review." Journal of Educational Studies in Mathematics and Computer Science 1, no. 2 (2024): 5–18. https://doi.org/10.5937/jesmac2402005i.

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In recent years, STEM education, which encompasses science, technology, engineering, and mathematics, has experienced significant and dynamic progress. These advancements are characterised by the proliferation of scientific knowledge and the development of cutting-edge educational resources that rely on artificial intelligence technology, among other factors. A promising avenue for advancing formal education is presented by intelligent tutoring systems, which offer intelligent instruction and feedback, thereby facilitating a more personalized and practical learning experience. This study explo

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Glaze, Andrew, Patricia Moyer-Packenham, and Max Longhurst. "Teachers' Conceptions of Mathematics and the Use of Intelligent Tutoring Systems, Calculators, Dynamic Geometry Software and Desmos in the Classroom." Journal of Computers in Mathematics and Science Teaching 40, no. 3 (2021): 201–27. https://doi.org/10.70725/601390aategx.

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The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey cont

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22

Toussaint, Ben-Manson, Vanda Luengo, and Francis Jambon. "Analyse de connaissances perceptivo-gestuelles dans un Système Tutoriel Intelligent." Sciences et Technologies de l'Information et de la Communication pour l'Éducation et la Formation 24, no. 1 (2017): 231–64. http://dx.doi.org/10.3406/stice.2017.1732.

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23

Lee, C. H., J. E. Biegel, and C. M. Dixon. "Student Performance Evaluation for a Simulation Based Intelligent Expert Tutoring System." Proceedings of the Human Factors Society Annual Meeting 32, no. 18 (1988): 1212–16. http://dx.doi.org/10.1177/154193128803201805.

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Intelligent tutoring systems offer an exciting new way to train people in areas of complex domains. A simulation-based training system provides the student with the opportunity to manipulate a system without the consequences of real life mistakes. The intelligence required in the tutoring system is focused on the tutor's ability to teach the student efficient, strategic responses. This tutoring demands that the tutor is aware of the student's current ability, specific fault areas, and preferred method of tutoring. Instructional decisions are made by assessing the student's performance. The uti

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24

Ford, Lindsey. "A new intelligent tutoring system." British Journal of Educational Technology 39, no. 2 (2008): 311–18. http://dx.doi.org/10.1111/j.1467-8535.2008.00819.x.

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25

Kulik, James A., and J. D. Fletcher. "Effectiveness of Intelligent Tutoring Systems." Review of Educational Research 86, no. 1 (2016): 42–78. http://dx.doi.org/10.3102/0034654315581420.

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26

Jerinic, Ljubomir, and Vladan Devedzic. "The friendly intelligent tutoring environment." ACM SIGCHI Bulletin 32, no. 1 (2000): 83–94. http://dx.doi.org/10.1145/333329.333353.

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27

Wu, Albert K. W., and M. C. Lee. "Intelligent tutoring systems as design." Computers in Human Behavior 14, no. 2 (1998): 209–20. http://dx.doi.org/10.1016/s0747-5632(98)00002-8.

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28

Schwind, Camilla B. "An intelligent language tutoring system." International Journal of Man-Machine Studies 33, no. 5 (1990): 557–79. http://dx.doi.org/10.1016/s0020-7373(05)80053-7.

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29

Blandford, A. E. "Intelligent tutoring systems: Lessons learned." Computers & Education 14, no. 6 (1990): 544–45. http://dx.doi.org/10.1016/0360-1315(90)90114-m.

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30

Leitch, Roy. "Teaching knowledge and intelligent tutoring." Computers & Education 20, no. 3 (1993): 281–82. http://dx.doi.org/10.1016/0360-1315(93)90030-m.

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31

Sussex, Roland. "TEACHING KNOWLEDGE AND INTELLIGENT TUTORING." Computer Assisted Language Learning 5, no. 3 (1992): 195–99. http://dx.doi.org/10.1080/0958822920050309.

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32

YAZDANI, MASOUD. "Intelligent tutoring systems: An overview." Expert Systems 3, no. 3 (1986): 154–63. http://dx.doi.org/10.1111/j.1468-0394.1986.tb00488.x.

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33

Anderson, John R., C. Franklin Boyle, Albert T. Corbett, and Matthew W. Lewis. "Cognitive modeling and intelligent tutoring." Artificial Intelligence 42, no. 1 (1990): 7–49. http://dx.doi.org/10.1016/0004-3702(90)90093-f.

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34

Johnson, W. Lewis. "Intelligent tutoring systems: Lessons learned." Artificial Intelligence 48, no. 1 (1991): 125–34. http://dx.doi.org/10.1016/0004-3702(91)90085-x.

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35

Baker, Ryan S. "Stupid Tutoring Systems, Intelligent Humans." International Journal of Artificial Intelligence in Education 26, no. 2 (2016): 600–614. http://dx.doi.org/10.1007/s40593-016-0105-0.

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36

Ohlsson, Stellan. "Some principles of intelligent tutoring." Instructional Science 14, no. 3-4 (1986): 293–326. http://dx.doi.org/10.1007/bf00051825.

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37

Anohina, Alla. "Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints." International Journal of Computers Communications & Control 2, no. 1 (2007): 48. http://dx.doi.org/10.15837/ijccc.2007.1.2336.

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The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelli

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38

Petrovica, Sintija. "DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS." Mokslas – Lietuvos ateitis 6, no. 2 (2014): 138–46. http://dx.doi.org/10.3846/mla.2014.18.

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Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s kno

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Ygnace, J. "Les systèmes de transport intelligent." Recherche - Transports - Sécurité 68 (September 2000): 87. http://dx.doi.org/10.1016/s0761-8980(00)90026-8.

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40

VanLEHN, KURT. "The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems." Educational Psychologist 46, no. 4 (2011): 197–221. http://dx.doi.org/10.1080/00461520.2011.611369.

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41

Phillips, Fred, and Benny G. Johnson. "Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording." Issues in Accounting Education 26, no. 1 (2011): 87–97. http://dx.doi.org/10.2308/iace.2011.26.1.87.

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ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Imp

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42

Buche, Cédric, Ronan Querrec, Pierre De Loor, Pierre Chevaillier, and Jacques Tisseau. "PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel." Techniques et sciences informatiques 28, no. 8 (2009): 1051–76. http://dx.doi.org/10.3166/tsi.28.1051-1076.

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43

WALKER, ERIN, NIKOL RUMMEL, and KENNETH R. KOEDINGER. "INTEGRATING COLLABORATION AND INTELLIGENT TUTORING DATA IN THE EVALUATION OF A RECIPROCAL PEER TUTORING ENVIRONMENT." Research and Practice in Technology Enhanced Learning 4, no. 3 (2022): 221–51. https://doi.org/10.58459/rptel.2009.4221-251.

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Intelligent tutoring systems have been successful at increasing student mathematics learning, but may be further improved with the addition of collaborative activities. We have extended the Cognitive Tutor Algebra, a successful intelligent tutoring system for individual learning, with a reciprocal peer tutoring activity designed to increase concep- tual learning. While using our peer tutoring environment, students take on tutor and tutee roles, and engage in both problem-solving actions and dialogue. In a classroom study, we randomly assigned 62 participants to three conditions (adaptive assis

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44

Petrovica, Sintija, and Alla Anohina-Naumeca. "Design and implementation of agent interaction mechanisms for emotionally intelligent tutoring systems." Applied Computer Systems 13, no. 1 (2012): 44–53. http://dx.doi.org/10.2478/v10312-012-0006-2.

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Abstract - Modern intelligent systems are built from intelligent agents, but usually agent interaction mechanisms are described at the general level omitting design and implementation details. The paper presents aspects of practical implementation of agent interaction mechanisms in JADE platform on the basis of the set of agents for modelling student’s emotions in intelligent tutoring systems. The concepts of an intelligent and affective tutoring system, agent, and agent interaction are explained, as well.

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45

Siemer, Julika, and Marios C. Angelides. "Integrating an Intelligent Tutoring Facility into a Gaming Simulation Environment." Journal of Information Technology 12, no. 3 (1997): 207–22. http://dx.doi.org/10.1177/026839629701200305.

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Gaming simulations and intelligent tutoring systems are both substantive research and development areas within the field of computer-based education and training which have the potential for mutual enhancement. This paper argues that the pedagogical effectiveness of gaming simulations can be increased through the integration of an intelligent tutoring facility and examines possible roles for such support within a gaming simulation environment. It then commences to present INTUITION, the implementation of the Metal Box Business Simulation game, that illustrates how an intelligent tutoring facil

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46

Xu, Wei, Ke Zhao, Yatao Li, and Zhenzhen Yi. "FUDAOWANG." International Journal of Distance Education Technologies 10, no. 3 (2012): 67–90. http://dx.doi.org/10.4018/jdet.2012070105.

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Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, called “FUDAOWANG.” The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective mature domain. Its characteristic is that the knowledge employed is the mature knowledge accepted by most people. FUDAOWANG uses automatic reasoning technology about objective mature problems to realize its intelligence. Based on the results of the automa

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47

PEK, PENG-KIAT, and KIM-LENG POH. "MAKING DECISIONS IN AN INTELLIGENT TUTORING SYSTEM." International Journal of Information Technology & Decision Making 04, no. 02 (2005): 207–33. http://dx.doi.org/10.1142/s0219622005001489.

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In computerized tutoring, the pace of instruction is related to the student's mastery levels of the learning objectives. The observable student's behavior that can be used to measure his knowledge is usually his responses to test items. Unobservable variables that are related to learner's motivation can affect learning but are difficult to quantify. In comparison with other decision-theoretic tutoring systems, the novelties of this research are: (1) the efficiency-centric approach to develop the Bayesian networks; (2) the formulation of utility values for different tutoring outcomes that are i

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48

Uriarte-Portillo, Aldo, Ramón Zatarain-Cabada, María Lucía Barrón-Estrada, María Blanca Ibáñez, and Lucía-Margarita González-Barrón. "Intelligent Augmented Reality for Learning Geometry." Information 14, no. 4 (2023): 245. http://dx.doi.org/10.3390/info14040245.

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This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle sc

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Huang, Mu-Jung. "Intelligent Diagnosing and Learning Agents for Intelligent Tutoring Systems." Journal of Computer Information Systems 40, no. 1 (1999): 45–50. http://dx.doi.org/10.1080/08874417.1999.11647422.

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FUKUHARA, Yoshimi, and Kiyoshi NAKABAYASHI. "Intelligent Tutoring System using WWW(CALAT)." Journal of JSEE 44, no. 3 (1996): 26–31. http://dx.doi.org/10.4307/jsee.44.3_26.

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